'''simple docstring'''

import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
 raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
 raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
A_     :						int    							=logging.get_logger(__name__)

A_     :						Any    							="The Nymphenburg Palace is a beautiful palace in Munich!"



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Tuple)			->					Optional[int]:
   lowerCAmelCase_			=      {
       '''attention_cell''': '''multi_head''',
       '''num_layers''': 4,
       '''units''': 1024,
       '''hidden_size''': 768,
       '''max_length''': 512,
       '''num_heads''': 8,
       '''scaled''': True,
       '''dropout''': 0.1,
       '''use_residual''': True,
       '''embed_size''': 1024,
       '''embed_dropout''': 0.1,
       '''word_embed''': None,
       '''layer_norm_eps''': 1E-5,
       '''token_type_vocab_size''': 2,
   }

   lowerCAmelCase_			=      bort_4_8_768_1024_hparams

   # Let's construct the original Bort model here
   # Taken from official BERT implementation, see:
   # https://github.com/alexa/bort/blob/master/bort/bort.py
   lowerCAmelCase_			=      BERTEncoder(
       attention_cell=predefined_args['''attention_cell''']	,   num_layers=predefined_args['''num_layers''']	,   units=predefined_args['''units''']	,   hidden_size=predefined_args['''hidden_size''']	,   max_length=predefined_args['''max_length''']	,   num_heads=predefined_args['''num_heads''']	,   scaled=predefined_args['''scaled''']	,   dropout=predefined_args['''dropout''']	,   output_attention=UpperCamelCase_	,   output_all_encodings=UpperCamelCase_	,   use_residual=predefined_args['''use_residual''']	,   activation=predefined_args.get('''activation'''	,   '''gelu''')	,   layer_norm_eps=predefined_args.get('''layer_norm_eps'''	,   UpperCamelCase_)	,   )

   # Vocab information needs to be fetched first
   # It's the same as RoBERTa, so RobertaTokenizer can be used later
   lowerCAmelCase_			=      '''openwebtext_ccnews_stories_books_cased'''

   # Specify download folder to Gluonnlp's vocab
   lowerCAmelCase_			=      os.path.join(get_home_dir()	,   '''models''')
   lowerCAmelCase_			=      _load_vocab(UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_	,   cls=UpperCamelCase_)

   lowerCAmelCase_			=      nlp.model.BERTModel(
       UpperCamelCase_	,   len(UpperCamelCase_)	,   units=predefined_args['''units''']	,   embed_size=predefined_args['''embed_size''']	,   embed_dropout=predefined_args['''embed_dropout''']	,   word_embed=predefined_args['''word_embed''']	,   use_pooler=UpperCamelCase_	,   use_token_type_embed=UpperCamelCase_	,   token_type_vocab_size=predefined_args['''token_type_vocab_size''']	,   use_classifier=UpperCamelCase_	,   use_decoder=UpperCamelCase_	,   )

   original_bort.load_parameters(UpperCamelCase_	,   cast_dtype=UpperCamelCase_	,   ignore_extra=UpperCamelCase_)
   lowerCAmelCase_			=      original_bort._collect_params_with_prefix()

   # Build our config 🤗
   lowerCAmelCase_			=      {
       '''architectures''': ['''BertForMaskedLM'''],
       '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
       '''hidden_act''': '''gelu''',
       '''hidden_dropout_prob''': predefined_args['''dropout'''],
       '''hidden_size''': predefined_args['''embed_size'''],
       '''initializer_range''': 0.0_2,
       '''intermediate_size''': predefined_args['''hidden_size'''],
       '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
       '''max_position_embeddings''': predefined_args['''max_length'''],
       '''model_type''': '''bort''',
       '''num_attention_heads''': predefined_args['''num_heads'''],
       '''num_hidden_layers''': predefined_args['''num_layers'''],
       '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
       '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
       '''vocab_size''': len(UpperCamelCase_),
   }

   lowerCAmelCase_			=      BertConfig.from_dict(UpperCamelCase_)
   lowerCAmelCase_			=      BertForMaskedLM(UpperCamelCase_)
   hf_bort_model.eval()

   # Parameter mapping table (Gluonnlp to Transformers)
   # * denotes layer index
   #
   # | Gluon Parameter                                                | Transformers Parameter
   # | -------------------------------------------------------------- | ----------------------
   # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
   # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
   # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
   # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
   # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
   # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
   # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
   # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
   # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
   # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
   # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
   # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
   # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
   # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
   # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
   # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
   # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
   # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
   # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
   # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

   # Helper function to convert MXNET Arrays to PyTorch
   def to_torch(__snake_case  :							List[str]) -> nn.Parameter:
      return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()))

   # Check param shapes and map new HF param back
   def check_and_map_params(__snake_case  :							Optional[Any]	,   __snake_case  :							Union[str, Any]):
      lowerCAmelCase_			=      hf_param.shape

      lowerCAmelCase_			=      to_torch(params[gluon_param])
      lowerCAmelCase_			=      gluon_param.shape

      assert (
          shape_hf == shape_gluon
      ), F'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

      return gluon_param

   lowerCAmelCase_			=      check_and_map_params(
       hf_bort_model.bert.embeddings.word_embeddings.weight	,   '''word_embed.0.weight''')
   lowerCAmelCase_			=      check_and_map_params(
       hf_bort_model.bert.embeddings.position_embeddings.weight	,   '''encoder.position_weight''')
   lowerCAmelCase_			=      check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.bias	,   '''encoder.layer_norm.beta''')
   lowerCAmelCase_			=      check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.weight	,   '''encoder.layer_norm.gamma''')

   # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
   lowerCAmelCase_			=      torch.zeros_like(
       hf_bort_model.bert.embeddings.token_type_embeddings.weight.data)

   for i in range(hf_bort_config.num_hidden_layers):
      lowerCAmelCase_			=      hf_bort_model.bert.encoder.layer[i]

      # self attention
      lowerCAmelCase_			=      layer.attention.self

      lowerCAmelCase_			=      check_and_map_params(
          self_attn.key.bias.data	,   F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''')

      lowerCAmelCase_			=      check_and_map_params(
          self_attn.key.weight.data	,   F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''')
      lowerCAmelCase_			=      check_and_map_params(
          self_attn.query.bias.data	,   F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''')
      lowerCAmelCase_			=      check_and_map_params(
          self_attn.query.weight.data	,   F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''')
      lowerCAmelCase_			=      check_and_map_params(
          self_attn.value.bias.data	,   F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''')
      lowerCAmelCase_			=      check_and_map_params(
          self_attn.value.weight.data	,   F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''')

      # self attention output
      lowerCAmelCase_			=      layer.attention.output

      lowerCAmelCase_			=      check_and_map_params(
          self_output.dense.bias	,   F'''encoder.transformer_cells.{i}.proj.bias''')
      lowerCAmelCase_			=      check_and_map_params(
          self_output.dense.weight	,   F'''encoder.transformer_cells.{i}.proj.weight''')
      lowerCAmelCase_			=      check_and_map_params(
          self_output.LayerNorm.bias	,   F'''encoder.transformer_cells.{i}.layer_norm.beta''')
      lowerCAmelCase_			=      check_and_map_params(
          self_output.LayerNorm.weight	,   F'''encoder.transformer_cells.{i}.layer_norm.gamma''')

      # intermediate
      lowerCAmelCase_			=      layer.intermediate

      lowerCAmelCase_			=      check_and_map_params(
          intermediate.dense.bias	,   F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''')
      lowerCAmelCase_			=      check_and_map_params(
          intermediate.dense.weight	,   F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''')

      # output
      lowerCAmelCase_			=      layer.output

      lowerCAmelCase_			=      check_and_map_params(
          bert_output.dense.bias	,   F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''')
      lowerCAmelCase_			=      check_and_map_params(
          bert_output.dense.weight	,   F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''')
      lowerCAmelCase_			=      check_and_map_params(
          bert_output.LayerNorm.bias	,   F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''')
      lowerCAmelCase_			=      check_and_map_params(
          bert_output.LayerNorm.weight	,   F'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma''')

   # Save space and energy 🎄
   hf_bort_model.half()

   # Compare output of both models
   lowerCAmelCase_			=      RobertaTokenizer.from_pretrained('''roberta-base''')

   lowerCAmelCase_			=      tokenizer.encode_plus(UpperCamelCase_)['''input_ids''']

   # Get gluon output
   lowerCAmelCase_			=      mx.nd.array([input_ids])
   lowerCAmelCase_			=      original_bort(inputs=UpperCamelCase_	,   token_types=[])

   # Get Transformer output (save and reload model again)
   hf_bort_model.save_pretrained(UpperCamelCase_)
   lowerCAmelCase_			=      BertModel.from_pretrained(UpperCamelCase_)
   hf_bort_model.eval()

   lowerCAmelCase_			=      tokenizer.encode_plus(UpperCamelCase_	,   return_tensors='''pt''')
   lowerCAmelCase_			=      hf_bort_model(**UpperCamelCase_)[0]

   lowerCAmelCase_			=      output_gluon[0].asnumpy()
   lowerCAmelCase_			=      output_hf[0].detach().numpy()

   lowerCAmelCase_			=      np.max(np.abs(hf_layer - gluon_layer)).item()
   lowerCAmelCase_			=      np.allclose(UpperCamelCase_	,   UpperCamelCase_	,   atol=1E-3)

   if success:
      print('''✔️ Both model do output the same tensors''')
   else:
      print('''❌ Both model do **NOT** output the same tensors''')
      print('''Absolute difference is:'''	,   UpperCamelCase_)


if __name__ == "__main__":
 A_     :						List[Any]    							=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 A_     :						Optional[int]    							=parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''laion/clap-htsat-unfused'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return RobertaTokenizer.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							ClapProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ClapProcessor(tokenizer=self.get_tokenizer()     ,   feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''       )
         snake_case											=							self.get_feature_extractor(do_normalize=__snake_case     ,   padding_value=1.0       )

         snake_case											=							ClapProcessor.from_pretrained(
             self.tmpdirname     ,   bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''     ,   do_normalize=__snake_case     ,   padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							floats_list((3, 1_0_0_0)       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audios=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							'''This is a test string'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names[2:]     ,   feature_extractor.model_input_names     ,   msg='''`processor` and `feature_extractor` model input names do not match'''     ,   )




